Overview

Dataset statistics

Number of variables13
Number of observations146076
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.6 MiB
Average record size in memory112.0 B

Variable types

Numeric12
Categorical1

Alerts

MonthlyIncome is highly overall correlated with MonthlyIncome_logHigh correlation
MonthlyIncome_log is highly overall correlated with MonthlyIncomeHigh correlation
default is highly imbalanced (64.4%)Imbalance
RevolvingUtilizationOfUnsecuredLines is highly skewed (γ1 = 99.15063964)Skewed
NumberOfTime30-59DaysPastDueNotWorse is highly skewed (γ1 = 23.60346321)Skewed
DebtRatio is highly skewed (γ1 = 101.4491464)Skewed
MonthlyIncome is highly skewed (γ1 = 125.4732483)Skewed
NumberOfTimes90DaysLate is highly skewed (γ1 = 24.17372735)Skewed
NumberOfTime60-89DaysPastDueNotWorse is highly skewed (γ1 = 24.45926564)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
RevolvingUtilizationOfUnsecuredLines has 10395 (7.1%) zerosZeros
NumberOfTime30-59DaysPastDueNotWorse has 122503 (83.9%) zerosZeros
DebtRatio has 3708 (2.5%) zerosZeros
MonthlyIncome has 1634 (1.1%) zerosZeros
NumberOfOpenCreditLinesAndLoans has 1731 (1.2%) zerosZeros
NumberOfTimes90DaysLate has 137922 (94.4%) zerosZeros
NumberRealEstateLoansOrLines has 53893 (36.9%) zerosZeros
NumberOfTime60-89DaysPastDueNotWorse has 138611 (94.9%) zerosZeros
NumberOfDependents has 86902 (59.5%) zerosZeros
MonthlyIncome_log has 1634 (1.1%) zerosZeros

Reproduction

Analysis started2025-03-02 20:35:31.392968
Analysis finished2025-03-02 20:35:52.975732
Duration21.58 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct146076
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75013.669
Minimum1
Maximum150000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-03-02T20:35:53.072729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7507.75
Q137534.75
median75013.5
Q3112496.25
95-th percentile142483.25
Maximum150000
Range149999
Interquartile range (IQR)74961.5

Descriptive statistics

Standard deviation43290.648
Coefficient of variation (CV)0.57710346
Kurtosis-1.1996371
Mean75013.669
Median Absolute Deviation (MAD)37481
Skewness-0.00032919376
Sum1.0957697 × 1010
Variance1.8740802 × 109
MonotonicityStrictly increasing
2025-03-02T20:35:53.240262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
100008 1
 
< 0.1%
100002 1
 
< 0.1%
100003 1
 
< 0.1%
100004 1
 
< 0.1%
100005 1
 
< 0.1%
100006 1
 
< 0.1%
100007 1
 
< 0.1%
100009 1
 
< 0.1%
100000 1
 
< 0.1%
Other values (146066) 146066
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
150000 1
< 0.1%
149999 1
< 0.1%
149998 1
< 0.1%
149997 1
< 0.1%
149996 1
< 0.1%
149995 1
< 0.1%
149994 1
< 0.1%
149993 1
< 0.1%
149992 1
< 0.1%
149991 1
< 0.1%

default
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
0
136229 
1
 
9847

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters146076
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 136229
93.3%
1 9847
 
6.7%

Length

2025-03-02T20:35:53.402186image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T20:35:53.521355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 136229
93.3%
1 9847
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 136229
93.3%
1 9847
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 146076
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 136229
93.3%
1 9847
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 146076
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 136229
93.3%
1 9847
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 146076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 136229
93.3%
1 9847
 
6.7%

RevolvingUtilizationOfUnsecuredLines
Real number (ℝ)

SKEWED  ZEROS 

Distinct122957
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.922272
Minimum0
Maximum50708
Zeros10395
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-03-02T20:35:53.786501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.031017866
median0.15881759
Q30.5636837
95-th percentile0.9999999
Maximum50708
Range50708
Interquartile range (IQR)0.53266584

Descriptive statistics

Standard deviation250.07077
Coefficient of variation (CV)42.225479
Kurtosis14833.824
Mean5.922272
Median Absolute Deviation (MAD)0.15234958
Skewness99.15064
Sum865101.81
Variance62535.392
MonotonicityNot monotonic
2025-03-02T20:35:53.954252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10395
 
7.1%
0.9999999 9822
 
6.7%
1 17
 
< 0.1%
0.9500998 8
 
< 0.1%
0.71314741 6
 
< 0.1%
0.954091816 6
 
< 0.1%
0.007984032 6
 
< 0.1%
0.004999 5
 
< 0.1%
0.796407186 5
 
< 0.1%
0.850299401 5
 
< 0.1%
Other values (122947) 125801
86.1%
ValueCountFrequency (%)
0 10395
7.1%
9.93 × 10-61
 
< 0.1%
1.25 × 10-51
 
< 0.1%
1.43 × 10-51
 
< 0.1%
1.49 × 10-51
 
< 0.1%
1.51 × 10-51
 
< 0.1%
1.6 × 10-51
 
< 0.1%
1.64 × 10-51
 
< 0.1%
1.87 × 10-51
 
< 0.1%
1.88 × 10-51
 
< 0.1%
ValueCountFrequency (%)
50708 1
< 0.1%
29110 1
< 0.1%
22198 1
< 0.1%
22000 1
< 0.1%
20514 1
< 0.1%
18300 1
< 0.1%
17441 1
< 0.1%
13930 1
< 0.1%
13498 1
< 0.1%
13400 1
< 0.1%

age
Real number (ℝ)

Distinct84
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.099277
Minimum0
Maximum107
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-03-02T20:35:54.124603image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q141
median52
Q362
95-th percentile77
Maximum107
Range107
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.604005
Coefficient of variation (CV)0.2803111
Kurtosis-0.49616164
Mean52.099277
Median Absolute Deviation (MAD)11
Skewness0.18953268
Sum7610454
Variance213.27698
MonotonicityNot monotonic
2025-03-02T20:35:54.285788image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 3782
 
2.6%
48 3741
 
2.6%
50 3705
 
2.5%
47 3671
 
2.5%
46 3660
 
2.5%
63 3621
 
2.5%
51 3566
 
2.4%
53 3566
 
2.4%
52 3535
 
2.4%
56 3497
 
2.4%
Other values (74) 109732
75.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
21 152
 
0.1%
22 396
 
0.3%
23 599
 
0.4%
24 760
0.5%
25 909
0.6%
26 1148
0.8%
27 1282
0.9%
28 1520
1.0%
29 1663
1.1%
ValueCountFrequency (%)
107 1
 
< 0.1%
103 3
 
< 0.1%
102 3
 
< 0.1%
101 3
 
< 0.1%
99 5
 
< 0.1%
98 5
 
< 0.1%
97 12
 
< 0.1%
96 14
 
< 0.1%
95 37
< 0.1%
94 35
< 0.1%

NumberOfTime30-59DaysPastDueNotWorse
Real number (ℝ)

SKEWED  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40794518
Minimum0
Maximum98
Zeros122503
Zeros (%)83.9%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-03-02T20:35:54.424471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.0027469
Coefficient of variation (CV)9.8119726
Kurtosis571.8254
Mean0.40794518
Median Absolute Deviation (MAD)0
Skewness23.603463
Sum59591
Variance16.021983
MonotonicityNot monotonic
2025-03-02T20:35:54.555851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 122503
83.9%
1 15745
 
10.8%
2 4538
 
3.1%
3 1741
 
1.2%
4 735
 
0.5%
5 340
 
0.2%
98 233
 
0.2%
6 138
 
0.1%
7 54
 
< 0.1%
8 25
 
< 0.1%
Other values (6) 24
 
< 0.1%
ValueCountFrequency (%)
0 122503
83.9%
1 15745
 
10.8%
2 4538
 
3.1%
3 1741
 
1.2%
4 735
 
0.5%
5 340
 
0.2%
6 138
 
0.1%
7 54
 
< 0.1%
8 25
 
< 0.1%
9 11
 
< 0.1%
ValueCountFrequency (%)
98 233
0.2%
96 5
 
< 0.1%
13 1
 
< 0.1%
12 2
 
< 0.1%
11 1
 
< 0.1%
10 4
 
< 0.1%
9 11
 
< 0.1%
8 25
 
< 0.1%
7 54
 
< 0.1%
6 138
0.1%

DebtRatio
Real number (ℝ)

SKEWED  ZEROS 

Distinct114090
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean333.3736
Minimum0
Maximum329664
Zeros3708
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-03-02T20:35:54.705276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0046451187
Q10.17176407
median0.35775058
Q30.76611694
95-th percentile2382
Maximum329664
Range329664
Interquartile range (IQR)0.59435287

Descriptive statistics

Standard deviation1943.9067
Coefficient of variation (CV)5.8310156
Kurtosis15848.527
Mean333.3736
Median Absolute Deviation (MAD)0.23336792
Skewness101.44915
Sum48697882
Variance3778773.2
MonotonicityNot monotonic
2025-03-02T20:35:54.869131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3708
 
2.5%
1 158
 
0.1%
4 129
 
0.1%
3 121
 
0.1%
2 117
 
0.1%
5 103
 
0.1%
9 101
 
0.1%
10 90
 
0.1%
8 90
 
0.1%
7 89
 
0.1%
Other values (114080) 141370
96.8%
ValueCountFrequency (%)
0 3708
2.5%
2.6 × 10-51
 
< 0.1%
3.69 × 10-51
 
< 0.1%
3.93 × 10-51
 
< 0.1%
6.62 × 10-51
 
< 0.1%
7.5 × 10-51
 
< 0.1%
8 × 10-51
 
< 0.1%
8.57 × 10-51
 
< 0.1%
9.09 × 10-51
 
< 0.1%
9.15 × 10-51
 
< 0.1%
ValueCountFrequency (%)
329664 1
< 0.1%
326442 1
< 0.1%
307001 1
< 0.1%
168835 1
< 0.1%
110952 1
< 0.1%
101320 1
< 0.1%
61907 1
< 0.1%
61106.5 1
< 0.1%
60212 1
< 0.1%
52112 1
< 0.1%

MonthlyIncome
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct13594
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6445.8134
Minimum0
Maximum3008750
Zeros1634
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-03-02T20:35:55.034024image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1500
Q13820
median5400
Q37500
95-th percentile13650
Maximum3008750
Range3008750
Interquartile range (IQR)3680

Descriptive statistics

Standard deviation13061.29
Coefficient of variation (CV)2.0263214
Kurtosis23633.919
Mean6445.8134
Median Absolute Deviation (MAD)1771
Skewness125.47325
Sum9.4157864 × 108
Variance1.7059729 × 108
MonotonicityNot monotonic
2025-03-02T20:35:55.201001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5400 26138
 
17.9%
5000 2757
 
1.9%
4000 2106
 
1.4%
6000 1934
 
1.3%
3000 1758
 
1.2%
0 1634
 
1.1%
2500 1551
 
1.1%
10000 1466
 
1.0%
3500 1360
 
0.9%
4500 1226
 
0.8%
Other values (13584) 104146
71.3%
ValueCountFrequency (%)
0 1634
1.1%
1 605
 
0.4%
2 6
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
3008750 1
< 0.1%
1794060 1
< 0.1%
1560100 1
< 0.1%
1072500 1
< 0.1%
835040 1
< 0.1%
730483 1
< 0.1%
702500 1
< 0.1%
699530 1
< 0.1%
649587 1
< 0.1%
629000 1
< 0.1%

NumberOfOpenCreditLinesAndLoans
Real number (ℝ)

ZEROS 

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5292793
Minimum0
Maximum58
Zeros1731
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-03-02T20:35:55.366165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q311
95-th percentile18
Maximum58
Range58
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.1495331
Coefficient of variation (CV)0.60374774
Kurtosis3.1099096
Mean8.5292793
Median Absolute Deviation (MAD)3
Skewness1.2156551
Sum1245923
Variance26.517691
MonotonicityNot monotonic
2025-03-02T20:35:55.523755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 13272
 
9.1%
7 12933
 
8.9%
5 12531
 
8.6%
8 12316
 
8.4%
9 11158
 
7.6%
4 11141
 
7.6%
10 9496
 
6.5%
3 8573
 
5.9%
11 8227
 
5.6%
12 6918
 
4.7%
Other values (48) 39511
27.0%
ValueCountFrequency (%)
0 1731
 
1.2%
1 4104
 
2.8%
2 6253
4.3%
3 8573
5.9%
4 11141
7.6%
5 12531
8.6%
6 13272
9.1%
7 12933
8.9%
8 12316
8.4%
9 11158
7.6%
ValueCountFrequency (%)
58 1
 
< 0.1%
57 2
 
< 0.1%
56 2
 
< 0.1%
54 4
< 0.1%
53 1
 
< 0.1%
52 3
< 0.1%
51 2
 
< 0.1%
50 2
 
< 0.1%
49 4
< 0.1%
48 6
< 0.1%

NumberOfTimes90DaysLate
Real number (ℝ)

SKEWED  ZEROS 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25069827
Minimum0
Maximum98
Zeros137922
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-03-02T20:35:55.666610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.9771968
Coefficient of variation (CV)15.864477
Kurtosis590.55581
Mean0.25069827
Median Absolute Deviation (MAD)0
Skewness24.173727
Sum36621
Variance15.818095
MonotonicityNot monotonic
2025-03-02T20:35:55.807215image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 137922
94.4%
1 5141
 
3.5%
2 1522
 
1.0%
3 656
 
0.4%
4 289
 
0.2%
98 233
 
0.2%
5 128
 
0.1%
6 79
 
0.1%
7 37
 
< 0.1%
8 21
 
< 0.1%
Other values (9) 48
 
< 0.1%
ValueCountFrequency (%)
0 137922
94.4%
1 5141
 
3.5%
2 1522
 
1.0%
3 656
 
0.4%
4 289
 
0.2%
5 128
 
0.1%
6 79
 
0.1%
7 37
 
< 0.1%
8 21
 
< 0.1%
9 19
 
< 0.1%
ValueCountFrequency (%)
98 233
0.2%
96 5
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
13 4
 
< 0.1%
12 2
 
< 0.1%
11 5
 
< 0.1%
10 8
 
< 0.1%
9 19
 
< 0.1%

NumberRealEstateLoansOrLines
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0297174
Minimum0
Maximum54
Zeros53893
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-03-02T20:35:55.944301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum54
Range54
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1327738
Coefficient of variation (CV)1.1000822
Kurtosis60.959245
Mean1.0297174
Median Absolute Deviation (MAD)1
Skewness3.4862651
Sum150417
Variance1.2831764
MonotonicityNot monotonic
2025-03-02T20:35:56.085169image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 53893
36.9%
1 51191
35.0%
2 31156
21.3%
3 6230
 
4.3%
4 2141
 
1.5%
5 678
 
0.5%
6 318
 
0.2%
7 170
 
0.1%
8 93
 
0.1%
9 78
 
0.1%
Other values (18) 128
 
0.1%
ValueCountFrequency (%)
0 53893
36.9%
1 51191
35.0%
2 31156
21.3%
3 6230
 
4.3%
4 2141
 
1.5%
5 678
 
0.5%
6 318
 
0.2%
7 170
 
0.1%
8 93
 
0.1%
9 78
 
0.1%
ValueCountFrequency (%)
54 1
 
< 0.1%
32 1
 
< 0.1%
29 1
 
< 0.1%
26 1
 
< 0.1%
25 3
< 0.1%
23 2
< 0.1%
21 1
 
< 0.1%
20 2
< 0.1%
19 2
< 0.1%
18 2
< 0.1%

NumberOfTime60-89DaysPastDueNotWorse
Real number (ℝ)

SKEWED  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2250267
Minimum0
Maximum98
Zeros138611
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-03-02T20:35:56.214178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.9620484
Coefficient of variation (CV)17.607015
Kurtosis600.29354
Mean0.2250267
Median Absolute Deviation (MAD)0
Skewness24.459266
Sum32871
Variance15.697827
MonotonicityNot monotonic
2025-03-02T20:35:56.343987image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 138611
94.9%
1 5647
 
3.9%
2 1103
 
0.8%
3 312
 
0.2%
98 233
 
0.2%
4 105
 
0.1%
5 32
 
< 0.1%
6 16
 
< 0.1%
7 8
 
< 0.1%
96 5
 
< 0.1%
Other values (3) 4
 
< 0.1%
ValueCountFrequency (%)
0 138611
94.9%
1 5647
 
3.9%
2 1103
 
0.8%
3 312
 
0.2%
4 105
 
0.1%
5 32
 
< 0.1%
6 16
 
< 0.1%
7 8
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
98 233
0.2%
96 5
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
8 2
 
< 0.1%
7 8
 
< 0.1%
6 16
 
< 0.1%
5 32
 
< 0.1%
4 105
 
0.1%
3 312
0.2%

NumberOfDependents
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75722227
Minimum0
Maximum20
Zeros86902
Zeros (%)59.5%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-03-02T20:35:56.468963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1150861
Coefficient of variation (CV)1.4726007
Kurtosis3.0016568
Mean0.75722227
Median Absolute Deviation (MAD)0
Skewness1.5882424
Sum110612
Variance1.2434169
MonotonicityNot monotonic
2025-03-02T20:35:56.602473image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 86902
59.5%
1 26316
 
18.0%
2 19522
 
13.4%
3 9483
 
6.5%
4 2862
 
2.0%
5 746
 
0.5%
6 158
 
0.1%
7 51
 
< 0.1%
8 24
 
< 0.1%
10 5
 
< 0.1%
Other values (3) 7
 
< 0.1%
ValueCountFrequency (%)
0 86902
59.5%
1 26316
 
18.0%
2 19522
 
13.4%
3 9483
 
6.5%
4 2862
 
2.0%
5 746
 
0.5%
6 158
 
0.1%
7 51
 
< 0.1%
8 24
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
13 1
 
< 0.1%
10 5
 
< 0.1%
9 5
 
< 0.1%
8 24
 
< 0.1%
7 51
 
< 0.1%
6 158
 
0.1%
5 746
 
0.5%
4 2862
 
2.0%
3 9483
6.5%

MonthlyIncome_log
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13594
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4434578
Minimum0
Maximum14.917036
Zeros1634
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-03-02T20:35:56.755928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.3138868
Q18.2482674
median8.5943394
Q38.9227916
95-th percentile9.5215681
Maximum14.917036
Range14.917036
Interquartile range (IQR)0.67452418

Descriptive statistics

Standard deviation1.2115051
Coefficient of variation (CV)0.14348447
Kurtosis30.913641
Mean8.4434578
Median Absolute Deviation (MAD)0.32845222
Skewness-4.8658389
Sum1233386.5
Variance1.4677445
MonotonicityNot monotonic
2025-03-02T20:35:56.914989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.594339401 26138
 
17.9%
8.517393171 2757
 
1.9%
8.294299609 2106
 
1.4%
8.699681401 1934
 
1.3%
8.006700845 1758
 
1.2%
0 1634
 
1.1%
7.824445931 1551
 
1.1%
9.210440367 1466
 
1.0%
8.160803921 1360
 
0.9%
8.412054873 1226
 
0.8%
Other values (13584) 104146
71.3%
ValueCountFrequency (%)
0 1634
1.1%
0.6931471806 605
 
0.4%
1.098612289 6
 
< 0.1%
1.609437912 2
 
< 0.1%
1.791759469 2
 
< 0.1%
2.079441542 1
 
< 0.1%
2.302585093 1
 
< 0.1%
2.397895273 2
 
< 0.1%
2.48490665 1
 
< 0.1%
2.772588722 1
 
< 0.1%
ValueCountFrequency (%)
14.9170356 1
< 0.1%
14.39999232 1
< 0.1%
14.26026112 1
< 0.1%
13.88550386 1
< 0.1%
13.6352361 1
< 0.1%
13.50146261 1
< 0.1%
13.4624021 1
< 0.1%
13.45816539 1
< 0.1%
13.38409359 1
< 0.1%
13.35188813 1
< 0.1%

Interactions

2025-03-02T20:35:50.780963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:34.074492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:35.608802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:37.127758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:38.524035image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:40.013751image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:41.679461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:43.130136image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:44.643215image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:46.103523image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:47.737732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:49.225661image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:50.909355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:34.212013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:35.738468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:37.247127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:38.653527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:40.139339image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:41.806619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:43.260612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:44.781265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-02T20:35:47.867050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:49.359735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-02T20:35:34.340053image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:35.859360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-02T20:35:34.578625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:36.111067image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:37.582718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-02T20:35:39.137734image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:40.817487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:42.278045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:43.756500image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-02T20:35:36.361799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-02T20:35:40.940628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-02T20:35:48.478522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:50.004770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:51.635460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:34.963161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:36.489601image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:37.931756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:39.390529image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:41.063346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:42.518150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:44.010871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:45.494984image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:46.966873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:48.605981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:50.136689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:51.764946image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:35.085486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:36.611651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:38.042110image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:39.510622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:41.180481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:42.634905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:44.132759image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:45.610075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:47.085354image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:48.725393image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:50.261141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:51.888062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:35.210135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:36.734650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:38.155864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-03-02T20:35:47.615541image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:49.101154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-02T20:35:50.650797image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2025-03-02T20:35:57.032922image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
DebtRatioMonthlyIncomeMonthlyIncome_logNumberOfDependentsNumberOfOpenCreditLinesAndLoansNumberOfTime30-59DaysPastDueNotWorseNumberOfTime60-89DaysPastDueNotWorseNumberOfTimes90DaysLateNumberRealEstateLoansOrLinesRevolvingUtilizationOfUnsecuredLinesUnnamed: 0agedefault
DebtRatio1.000-0.097-0.097-0.0380.2480.0440.005-0.0310.4220.091-0.0010.0190.000
MonthlyIncome-0.0971.0001.0000.1860.278-0.014-0.048-0.0800.352-0.0670.0010.1100.000
MonthlyIncome_log-0.0971.0001.0000.1860.278-0.014-0.048-0.0800.352-0.0670.0010.1100.053
NumberOfDependents-0.0380.1860.1861.0000.1000.0710.0350.0300.1660.118-0.000-0.2280.031
NumberOfOpenCreditLinesAndLoans0.2480.2780.2780.1001.0000.061-0.049-0.1360.469-0.0930.0040.1690.052
NumberOfTime30-59DaysPastDueNotWorse0.044-0.014-0.0140.0710.0611.0000.2790.2510.0200.2350.001-0.0920.083
NumberOfTime60-89DaysPastDueNotWorse0.005-0.048-0.0480.035-0.0490.2791.0000.318-0.0460.1870.001-0.0830.081
NumberOfTimes90DaysLate-0.031-0.080-0.0800.030-0.1360.2510.3181.000-0.1030.237-0.005-0.1020.084
NumberRealEstateLoansOrLines0.4220.3520.3520.1660.4690.020-0.046-0.1031.000-0.033-0.0020.0640.032
RevolvingUtilizationOfUnsecuredLines0.091-0.067-0.0670.118-0.0930.2350.1870.237-0.0331.000-0.005-0.2720.000
Unnamed: 0-0.0010.0010.001-0.0000.0040.0010.001-0.005-0.002-0.0051.0000.0050.007
age0.0190.1100.110-0.2280.169-0.092-0.083-0.1020.064-0.2720.0051.0000.114
default0.0000.0000.0530.0310.0520.0830.0810.0840.0320.0000.0070.1141.000

Missing values

2025-03-02T20:35:52.301772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-02T20:35:52.654733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0defaultRevolvingUtilizationOfUnsecuredLinesageNumberOfTime30-59DaysPastDueNotWorseDebtRatioMonthlyIncomeNumberOfOpenCreditLinesAndLoansNumberOfTimes90DaysLateNumberRealEstateLoansOrLinesNumberOfTime60-89DaysPastDueNotWorseNumberOfDependentsMonthlyIncome_log
0110.7661274520.8029829120.0130602.09.118335
1200.9571514000.1218762600.040001.07.863651
2300.6581803810.0851133042.021000.08.020599
3400.2338103000.0360503300.050000.08.101981
4500.9072394910.02492663588.070100.011.060196
5600.2131797400.3756073500.030101.08.160804
6700.3056825705710.0000005400.080300.08.594339
7800.7544643900.2099403500.080000.08.160804
91000.1891695700.60629123684.090402.010.072597
101100.6442263000.3094762500.050000.07.824446
Unnamed: 0defaultRevolvingUtilizationOfUnsecuredLinesageNumberOfTime30-59DaysPastDueNotWorseDebtRatioMonthlyIncomeNumberOfOpenCreditLinesAndLoansNumberOfTimes90DaysLateNumberRealEstateLoansOrLinesNumberOfTime60-89DaysPastDueNotWorseNumberOfDependentsMonthlyIncome_log
14999014999100.0555184600.6097794335.070102.08.374708
14999114999200.1041125900.47765810316.0100200.09.241548
14999214999300.8719765004132.0000005400.0110103.08.594339
14999314999401.0000002200.000000820.010000.06.710523
14999414999500.3857425000.4042933400.070000.08.131825
14999514999600.0406747400.2251312100.040100.07.650169
14999614999700.2997454400.7165625584.040102.08.627840
14999714999800.2460445803870.0000005400.0180100.08.594339
14999814999900.0000003000.0000005716.040000.08.651199
14999915000000.8502836400.2499088158.080200.09.006877